SVM based micro-blog emotion classification method fusing various kinds of emotion resources

Abstract

The invention discloses an SVM based micro-blog emotion classification method fusing various kinds of emotion resources. The method includes the following steps: constructing relevant dictionaries including an emotion dictionary, a negation dictionary, and a degree adverb dictionary; performing pretreatment on different corpora, performing word segmentation and part-of-speech tagging on the corpora, and performing sentence structure analysis; comparing the segmented words and positive and negative dictionaries to acquire initial word polarity, comparing words ahead of emotion words and the word degree grade dictionary and the negation dictionary to acquire modifier weight, and multiplying the initial word polarity by the modifier weight to acquire emotion scores of each micro-blog; extracting features such as nouns, verbs, adjectives, positive and negative emotion words, degree adverb weights, emotion scores, privatives and specific symbols from part-of-speech features, emotion features, sentence pattern features, and semantic features; and inputting the extracted features into an Libsvm to perform model training so as to acquire a training model. The method can achieve emotion 5-grade classification of micro-blogs, and can accurately and roundly acquire emotion tendency of netizens.

Description

technical field [0001] The invention belongs to the technical field of natural language processing, in particular to a microblog emotion classification method based on SVM and integrating multiple emotion resources. Background technique [0002] There are extremely rich subjective emotional information of netizens hidden in Weibo. By classifying microblogs and obtaining the emotional tendencies of the majority of netizens, we can quickly and accurately understand the demands of the majority of netizens and provide a reliable basis for network public opinion analysis. At present, many scholars have studied the sentiment classification of Weibo, mainly using the method based on the sentiment dictionary and the method of machine learning to divide the sentiment into three categories: positive, negative or positive, neutral and negative. The method based on the sentiment dictionary is to construct a sentiment dictionary, then calculate the sentiment tendency value through a spe...

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Problems solved by technology

Therefore, dividing emotions into three types is too absolute and cannot accurately and comprehensively capture the emotional tendencies of netizens

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